Complexity in Human and Humanoid Biomechanics

We propose the following complexity conjecture: in a combined biomechanical system, where the action of Newtonian laws cannot be neglected, it is the mechanical part that determines the lower limit of complexity of the combined system, commonly defined as the number of mechanical degrees of freedom. The biological part of such a system, being "more intelligent", naturally serves as a "controller" for the "nonintelligent" mechanical "plant". Although, in some special cases, the behavior of the combined system might have a "simple" output, a realistic internal state space analysis shows that the total system complexity represents either the superposition, or a kind of "macroscopic entanglement" of the two partial complexities. Neither "mutual canceling" nor "averaging" of the mechanical degrees of freedom generally occurs in such a biomechanical system. The combined system has both dynamical and control complexities. The "realistic" computational model of such a system also has its own computational complexity. We demonstrate the validity of the above conjecture using the example of the physiologically realistic computer model. We further argue that human motion is the simplest well-defined example of a general human behavior, and discuss issues of simplicity versus predictability/controllability in complex systems. Further, we discuss self-assembly in relation to conditioned training in human/humanoid motion. It is argued that there is a significant difference in the observational resolution of human motion while one is watching "subtle" movements of a human hands playing a piano versus "coarse" movements of a human crowd at a football stadium from an orbital satellite. Techniques such as cellular automata can model the coarse crowd motion, but not the subtle hierarchical neural control of the dynamics of human hands playing a piano. Therefore, we propose the observational resolution as a new measure of biomechanical complexity. Finally, there is a possible route to apparent simplicity in biomechanics, in the form of oscillatory synchronization, both external (kinematical) and internal (control).

[1]  Giuseppe Vitiello,et al.  Quantum noise, entanglement and chaos in the quantum field theory of mind/brain states , 2003, q-bio/0309009.

[2]  C. Pearce,et al.  Topological duality in humanoid robot dynamics , 2001, The ANZIAM Journal.

[3]  V. Ivancevic GENERALIZED HAMILTONIAN BIODYNAMICS AND TOPOLOGY INVARIANTS OF HUMANOID ROBOTS , 2002 .

[4]  Yaneer Bar-Yam,et al.  Multiscale Complexity/Entropy , 2004, Adv. Complex Syst..

[5]  J C Houk,et al.  Regulation of stiffness by skeletomotor reflexes. , 1979, Annual review of physiology.

[6]  N. A. Bernshteĭn The co-ordination and regulation of movements , 1967 .

[7]  A. Barto,et al.  Models of the cerebellum and motor learning , 1996 .

[8]  Alberto Isidori,et al.  Nonlinear control systems: an introduction (2nd ed.) , 1989 .

[9]  N. A. Bernstein Dexterity and Its Development , 1996 .

[10]  V. G. Ivancevic,et al.  Fuzzy-stochastic functor machine for general humanoid-robot dynamics , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[11]  A. Isidori,et al.  Adaptive control of linearizable systems , 1989 .

[12]  Bart Kosko,et al.  Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence , 1991 .

[13]  Stephen Wolfram,et al.  Cellular automata as models of complexity , 1984, Nature.

[14]  Vladimir G. Ivancevic,et al.  Brain-like functor control machine for general humanoid biodynamics , 2005, Int. J. Math. Math. Sci..

[15]  Vladimir Ivancevic,et al.  Symplectic Rotational Geometry in Human Biomechanics , 2004, SIAM Rev..

[16]  Eugene M. Izhikevich,et al.  “Subcritical Elliptic Bursting of Bautin Type ” (Izhikevich (2000b)). The following , 2022 .

[17]  Giuseppe Vitiello,et al.  Quantum noise induced entanglement and chaos in the dissipative quantum model of brain , 2004, quant-ph/0406161.

[18]  Miomir Vukobratovic,et al.  Contribution to the Study of Anthropomorphism of Humanoid Robots , 2005, Int. J. Humanoid Robotics.

[19]  VLADIMIR IVANCEVIC Lie-Lagrangian Model for Realistic Human Biodynamics , 2006, Int. J. Humanoid Robotics.

[20]  Jonghoon Park,et al.  Geometric integration on Euclidean group with application to articulated multibody systems , 2005, IEEE Transactions on Robotics.

[21]  Yaneer Bar-Yam,et al.  Dynamics Of Complex Systems , 2019 .

[22]  A. Hodgkin,et al.  A quantitative description of membrane current and its application to conduction and excitation in nerve , 1952, The Journal of physiology.